A simple procedure for the day‐to‐day forecasting of runoff from snow‐melt

Linsley, Ray K.

doi:10.1029/tr024i003p00062

Cited by 24 publications

(17 citation statements)

References 6 publications

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“…In another early application of the method, Linsley (1943) found it difficult to determine the actual volumes of snowmelt, so he determined the degree-day ratios (plotted in Figure 2) from temperatures and the basin runoff. The low average ratio for March of 0.1 cm CC-' d-' contrasts with the high ratio by the end of June of 0.7 cm CC-' d-' for two reasons: (1) the runoff in the early part of the snowmelt season does not contain all the meitwater (the remaining meltwater follows later as recession flow and increases the runoff in the latter part of the snowmelt season; and (2) the degree-day ratio from Equation (1) gradually increases as the snow becomes wet, and the decreasing albedo enhances the heat gain from the increasing solar radiation.…”

Section: Applications Of the Degree-day Methodsmentioning

confidence: 99%

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REVISITING THE DEGREE‐DAY METHOD FOR SNOWMELT COMPUTATIONS¹

Rango¹,

Martinec²

1995

J American Water Resour Assoc

276

201

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The simple, empirical degree‐day approach for calculating snowmelt and runoff from mountain basins has been in use for more than 60 years. It is frequently suggested that the degree‐day method be replaced by the more physically‐based energy balance approach. The degree‐day approach, however, maintains its popularity, applicability, and effectiveness. It is shown that the degree‐day method is reliable for computing total snowmelt depths for periods of a week to the entire snowmelt season. It can also be used for daily snowmelt depths when utilized in connection with an adequate snowmelt runoff model for computing the basin runoff. The degree‐day ratio is shown to vary seasonally as opposed to being constant as is often assumed. Additionally, in order to evaluate the degree‐day ratio correctly, the changing snow cover extent in a basin during the snowmelt season must be taken into account. It is also possible to combine the degree‐day approach with a radiation component so that short time interval (<24 hours) computations of snowmelt depth can be made. When snowmelt input is transformed to basin output (runoff) by a snowmelt runoff model, there is little difference between the degree‐day approach and a radiation‐based approach. This is fortuitous because the physically‐based energy balance models will not soon displace the degree‐day methods because of their excessive data requirements.

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Section: Applications Of the Degree-day Methodsmentioning

confidence: 99%

“…Degree-Day Factors Derived from Basin Runoff During Snowmelt in the San Joaquin River Basin, California (afterLinsley, 1943).…”

mentioning

confidence: 99%

REVISITING THE DEGREE‐DAY METHOD FOR SNOWMELT COMPUTATIONS¹

Rango¹,

Martinec²

1995

J American Water Resour Assoc

276

201

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“…Spring snowmelt contributes up to 80% of water for various water uses in the Canadian Prairies. Therefore, accurate prediction of basin snowmelt runoff in temperate and high latitudes is necessary for effective water resources management [ Linsley , 1943] and climate prediction studies.…”

Section: Introductionmentioning

confidence: 99%

Evaluating a hierarchy of snowmelt models at a watershed in the Canadian Prairies

2009

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[1] Three semidistributed snowmelt models (SDSM) were developed and applied to the Paddle River Basin (PRB) in the Canadian Prairies: (1) A physics-based, energy balance model (SDSM-EBM) that considers vertical energy exchange processes in open and forested areas, and snowmelt processes that include liquid and ice phases separately; (2) a modified temperature index model (SDSM-MTI) that uses both near surface soil temperature (T g ) and air temperature (T a ); and (3) a standard temperature index (SDSM-TI) method using T a only. Other than the ''regulatory'' effects of beaver dams that affected the validation results on simulated runoff, both SDSM-MTI and SDSM-EBM simulated reasonably accurate snowmelt runoff, snow water equivalent, and snow depth. For the PRB, where snowpack is shallow to moderately deep and winter is relatively severe, the advantage of using both T a and T g is partly attributed to T g showing a stronger correlation with solar radiation than T a during the spring snowmelt season and partly attributed to the onset of major snowmelt which usually happens when T g approaches 0°C.After resetting model parameters so that SDSM-MTI degenerated to SDSM-TI (the effect of T g is completely removed), the model performance worsened, even after recalibrating the melt factors using T a alone. It seems that if reliable T g data are available, they should be utilized to model the snowmelt processes in a prairie environment, particularly if the temperature-index approach is adopted.

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“…The fourth and final index, DD Ͼ 0, measures the extent to which the daily average temperature exceeds the melting point (with daily values below 0°C set to zero), and JFM DD Ͼ 0 is the sum of each individual day's DD Ͼ 0. Quantifying snowmelt from DD Ͼ 0 is the simplest approach used in snowmelt-runoff models when data on surface energy balances are not available (see, e.g., Linsley 1943).…”

Section: Observational and Model Data A Observational Datamentioning

confidence: 99%

Detection and Attribution of Temperature Changes in the Mountainous Western United States

et al. 2008

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Large changes in the hydrology of the western United States have been observed since the mid-twentieth century. These include a reduction in the amount of precipitation arriving as snow, a decline in snowpack at low and midelevations, and a shift toward earlier arrival of both snowmelt and the centroid (center of mass) of streamflows. To project future water supply reliability, it is crucial to obtain a better understanding of the underlying cause or causes for these changes. A regional warming is often posited as the cause of these changes without formal testing of different competitive explanations for the warming. In this study, a rigorous detection and attribution analysis is performed to determine the causes of the late winter/early spring changes in hydrologically relevant temperature variables over mountain ranges of the western United States. Natural internal climate variability, as estimated from two long control climate model simulations, is insufficient to explain the rapid increase in daily minimum and maximum temperatures, the sharp decline in frost days, and the rise in degree-days above 0°C (a simple proxy for temperature-driven snowmelt). These observed changes are also inconsistent with the model-predicted responses to variability in solar irradiance and volcanic activity. The observations are consistent with climate simulations that include the combined effects of anthropogenic greenhouse gases and aerosols. It is found that, for each temperature variable considered, an anthropogenic signal is identifiable in observational fields. The results are robust to uncertainties in model-estimated fingerprints and natural variability noise, to the choice of statistical downscaling method, and to various processing options in the detection and attribution method.

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A simple procedure for the day‐to‐day forecasting of runoff from snow‐melt

Cited by 24 publications

References 6 publications

REVISITING THE DEGREE‐DAY METHOD FOR SNOWMELT COMPUTATIONS¹

REVISITING THE DEGREE‐DAY METHOD FOR SNOWMELT COMPUTATIONS¹

Evaluating a hierarchy of snowmelt models at a watershed in the Canadian Prairies

Detection and Attribution of Temperature Changes in the Mountainous Western United States

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A simple procedure for the day‐to‐day forecasting of runoff from snow‐melt

Cited by 24 publications

References 6 publications

REVISITING THE DEGREE‐DAY METHOD FOR SNOWMELT COMPUTATIONS1

REVISITING THE DEGREE‐DAY METHOD FOR SNOWMELT COMPUTATIONS1

Evaluating a hierarchy of snowmelt models at a watershed in the Canadian Prairies

Detection and Attribution of Temperature Changes in the Mountainous Western United States

Contact Info

Product

Resources

About

REVISITING THE DEGREE‐DAY METHOD FOR SNOWMELT COMPUTATIONS¹

REVISITING THE DEGREE‐DAY METHOD FOR SNOWMELT COMPUTATIONS¹